2012 Italian Stata Users Group meeting: Abstracts

Handling missing data in Stata—a whirlwind tour

Jonathan Bartlett

London School of Hygiene and Tropical Medicine

Missing data is a pervasive issue in epidemiological, clinical, social, and
economic studies. This presentation offers a brief overview of the
conceptual issues raised by missing data, followed by an overview of some of
the principled statistical methods of handling missing data that are
implemented in Stata 12, including multiple imputation and inverse
probability weighting.

Maternal characteristics, childhood growth, and eating disorders: a study of mediation using gformula

Working in the margins to plot a clear course

Bill Rising

StataCorp LP

Visualizing the true effect of a predictor over a range of values can be
difficult for models that are not parameterized in their natural metric,
such as logistic or (even more so) probit models. Interaction terms in
such models cause even more fogginess. This illustrates how both the margins
and the marginsplot commands can make for much clearer explanations
of effects for both nonstatisticians and statisticians alike.